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theory, and the analysis of large data ensembles. You will write papers for submission to academic journals, collaborate with academics and PhD students, and communicate your research at national
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optimization techniques, coding new algorithms, creating new mathematical theory, and the analysis of large data ensembles. You will write papers for submission to academic journals, collaborate with academics
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Applications are invited for a fully funded, full-time PhD studentship in the Department of Mechanical and Aerospace Engineering, supported by Vestas Technology (UK) Ltd, one of the largest wind
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research takes a transdiagnostic approach, focusing on common mental health symptoms and identifying both risk and resilience factors. Using large-scale longitudinal data from over 114,500 participants, we
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-time, large-scale data demands of IIoT and lack sustainability considerations, such as energy efficiency. This research proposes a sustainable, high-performance IDS that leverages digital twin technology
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part of the wider group of CDP funded students across the UK, with access to events and training delivered in partnership with a range of cultural heritage institutions. Project Overview This PhD project